The goal is to use the graph to learn the kinds of unknowns that pertain to different concepts, as well as to exploit relationships between problems and answers.
We construct a graph
with three types of nodes: concept, problem; and answer nodes. We have 5 edge types: problem-has-type (between problems and concepts); problems-has-answer (between problems and answers); and same-section-as, mentioned-in-before-chapters, same-chapter-as (all three between concepts). We use the concept hierarchy derived from the ordering of concepts in [1]} to obtain the last three relationships. Our graph consists of
\(2,360\)  nodes, \(5,116\)  edges, and 768 initial node features obtained from BERT embeddings. Node features are from average pooling the embeddings. For the concept nodes, the pooled tokens are taken from the concept definitions.
The task for learning node representations is predicting the problem-has-type links.
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